The original kernel extreme learning machine (KELM) employs all training samples to construct hidden layer, thus avoiding the performance fluctuations caused by the ELM randomly assigning weights. However, excessive nodes will inevitably lead to structural redundancy, which hinders its application in systems with high real-time performance requirements but limited onboard storage and computing capacity. Considering the well interpretability of sparse learning, this study introduces the group sparse structure for KELM to resolve its limitation of structural redundancy. Specifically, the proposed novel method introduces a special norm to reformulate the dual optimization problem of KELM to realize group sparse structure in output weights. As a result, nodes with large weights can be selected as the significant nodes, while nodes with small weights will be regarded as the redundant nodes and neglected directly. In addition, we have also devised an alternating iterative optimization algorithm and deduced the complete proof of convergence to solve the non-smoothness optimization problem in proposed method. Then, the validity and feasibility of the proposed method are verified by extensive experiments on benchmark datasets. More importantly, tests of fault diagnosis for an aircraft engine show that the proposed approach can maintain the competitive recognition performance with much faster testing speed.

原始的内核极限学习机（KELM）使用所有训练样本来构造隐藏层，从而避免了ELM随机分配权重导致的性能波动。但是，过多的节点将不可避免地导致结构冗余，这会妨碍其在对实时性能有较高要求但机载存储和计算能力有限的系统中的应用。考虑到稀疏学习的良好可解释性，本研究介绍了KELM的组稀疏结构，以解决其结构冗余的局限性。具体而言，提出的新方法引入了一种特殊的范式来重新构造KELM的对偶优化问题，以实现输出权重中的组稀疏结构。结果，可以选择权重较大的节点作为有效节点，权重较小的节点将被视为冗余节点，并被直接忽略。此外，我们还设计了一种交替迭代优化算法，推导了收敛的完整证明，以解决所提出方法中的非光滑性优化问题。然后，通过在基准数据集上的大量实验，验证了该方法的有效性和可行性。更重要的是，对飞机发动机的故障诊断测试表明，该方法可以以更快的测试速度保持竞争性识别性能。在基准数据集上进行了广泛的实验，验证了该方法的有效性和可行性。更重要的是，对飞机发动机的故障诊断测试表明，该方法可以以更快的测试速度保持竞争性识别性能。在基准数据集上进行了广泛的实验，验证了该方法的有效性和可行性。更重要的是，对飞机发动机的故障诊断测试表明，该方法可以以更快的测试速度保持竞争性识别性能。